# Copyright 2019 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Reinforcement Learning (RL) Agent Base for Open Spiel."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import abc
import collections

StepOutput = collections.namedtuple("step_output", ["action", "probs"])


class AbstractAgent(metaclass=abc.ABCMeta):
    """Abstract base class for Open Spiel RL agents."""

    @abc.abstractmethod
    def __init__(self,
                 player_id,
                 session=None,
                 observation_spec=None,
                 name="agent",
                 **agent_specific_kwargs):
        """Initializes agent.

        Args:
          player_id: integer, mandatory. Corresponds to the player position in the
            game and is used to index the observation list.
          session: optional Tensorflow session.
          observation_spec: optional dict containing observation specifications.
          name: string. Must be used to scope TF variables. Defaults to `agent`.
          **agent_specific_kwargs: optional extra args.
        """

    @abc.abstractmethod
    def step(self, time_step, is_evaluation=False):
        """Returns action probabilities and chosen action at `time_step`.

        Agents should handle `time_step` and extract the required part of the
        `time_step.observations` field. This flexibility enables algorithms which
        rely on opponent observations / information, e.g. CFR.

        `is_evaluation` can be used so agents change their behaviour for evaluation
        purposes, e.g.: preventing exploration rate decaying during mytest and
        insertion of data to replay buffers.

        Arguments:
          time_step: an instance of rl_environment.TimeStep.
          is_evaluation: bool indicating whether the step is an evaluation routine,
            as opposed to a normal training step.

        Returns:
          A `StepOutput` for the current `time_step`.
        """
